Unlike random forests, where the decision trees are constructed in parallel prior to combination, GBDT employs a boosting approach,
in which each tree is sequentially trained with the aim of correcting the error produced by its predecessor. In particular, every tree is trained to learn the residual between the desired output and the output of the previous tree, using gradient descent.The most important parameter in GBDT is the number of base classifiers which controls the model complexity. The most recent and efficient GBDT methods developed are XGBoost [1]}, LightGBM [2]}, and CatBoost [3]}. These algorithms differ from each other in terms of the mechanism used for splitting the tree nodes.
